System and method for prioritizing medical conditions

ABSTRACT

A system for ordering and prioritizing multiple health disorders for automated remote patient care is presented. A database maintains information for an individual patient by organizing monitoring sets in a database, and measures relating to patient information previously recorded and derived on a substantially continuous basis into a monitoring set in the database. A server retrieving and processing the monitoring includes a comparison module comparing stored measures from each of the monitoring sets to other stored measures from another of the monitoring sets with both stored measures relating to the same type of patient information, and an analysis module ordering each patient status change in temporal sequence and categorizing health disorder candidates by quantifiable physiological measures, and identifying the health disorder candidate having the pathophysiology substantially corresponding to the patient status changes which occurred substantially least recently as the index disorder.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a divisional of Ser. No. 10/976,665, filedOct. 29, 2004, now U.S. Pat. No. 7,117,028, issued on Oct. 3, 2006,which is a continuation of Ser. No. 10/646,112, filed Aug. 22, 2003, nowU.S. Pat. No. 6,951,539, issued Oct. 4, 2005, which is a continuation ofSer. No. 10/210,418,filed Jul. 31, 2002, now U.S. Pat. No. 6,834,203,issued Dec. 21, 2004, which is a continuation of Ser. No. 09/441,405,filed Nov. 16, 1999, now U.S. Pat. No. 6,440,066, issued Aug. 27, 2002,the disclosures of which are incorporated by reference, and the priorityfiling dates of which are claimed.

FIELD OF THE INVENTION

The present invention relates in general to automated multiplenear-simultaneous health disorder diagnosis and analysis, and, inparticular, to an automated collection and analysis patient care systemand method for ordering and prioritizing multiple health disorders toidentify an index disorder.

BACKGROUND OF THE INVENTION

The rising availability of networked digital communications means,particularly wide area networks (WANs), including public informationinternetworks such as the Internet, have made possible diverseopportunities for providing traditional storefront- or office-boundservices through an automated and remote distributed system arrangement.For example, banking, stock trading, and even grocery shopping can nowbe performed on-line over the Internet. However, some forms of services,especially health care services which include disease diagnosis andtreatment, require detailed and personal knowledge of theconsumer/patient. The physiological data that would allow assessment ofa disease has traditionally been obtained through the physical presenceof the individual at the physician's office or in the hospital.

Presently, important physiological measures can be recorded andcollected for patients equipped with an external monitoring ortherapeutic device, or via implantable device technologies, or recordedmanually by the patient. If obtained frequently and regularly, theserecorded physiological measures can provide a degree of diseasedetection and prevention heretofore unknown. For instance, patientsalready suffering from some form of treatable heart disease oftenreceive an implantable pulse generator (IPG), cardiovascular or heartfailure monitor, therapeutic device, or similar external wearabledevice, with which rhythm and structural problems of the heart can bemonitored and treated. These types of devices are useful for detectingphysiological changes in patient conditions through the retrieval andanalysis of telemetered signals stored in an on-board, volatile memory.Typically, these devices can store more than thirty minutes of perheartbeat data recorded on a per heartbeat, binned average basis, or ona derived basis from which can be measured or derived, for example,atrial or ventricular electrical activity, minute ventilation, patientactivity score, cardiac output score, mixed venous oxygen score,cardiovascular pressure measures, and the like. However, the properanalysis of retrieved telemetered signals requires detailed medicalsubspecialty knowledge in the area of heart disease, such as bycardiologists and cardiac electrophysiologists.

Alternatively, these telemetered signals can be remotely collected andanalyzed using an automated patient care system. One such system isdescribed in a related, commonly assigned U.S. Pat. No. 6,312,378,issued Nov. 6, 2001, the disclosure of which is incorporated herein byreference. A medical device adapted to be implanted in an individualpatient records telemetered signals that are then retrieved on aregular, periodic basis using an interrogator or similar interfacingdevice. The telemetered signals are downloaded via an internetwork ontoa network server on a regular, e.g., daily, basis and stored as sets ofcollected measures in a database along with other patient care records.The information is then analyzed in an automated fashion and feedback,which includes a patient status indicator, is provided to the patient.

While such an automated system can serve as a valuable tool in providingremote patient care, an approach to systematically correlating andanalyzing the raw collected telemetered signals, as well as manuallycollected physiological measures, through applied medical knowledge toaccurately diagnose, order and prioritize multiple near-simultaneoushealth disorders, such as, by way of example, congestive heart failure,myocardial ischemia, respiratory insufficiency, and atrial fibrillation,is needed. As a case in point, a patient might develop pneumonia that inturn triggers the onset of myocardial ischemia that in turn leads tocongestive heart failure that in turn causes the onset of atrialfibrillation that in turn exacerbates all three preceding conditions.The relative relationship of the onset and magnitude of each diseasemeasure abnormality has direct bearing on the optimal course of therapy.Patients with one or more pre-existing diseases often present with aconfusing array of problems that can be best sorted and addressed byanalyzing the sequence of change in the various physiological measuresmonitored by the device.

One automated patient care system directed to a patient-specificmonitoring function is described in U.S. Pat. No. 5,113,869 ('869) toNappholz et al. The '869 patent discloses an implantable, programmableelectrocardiography (ECG) patient monitoring device that senses andanalyzes ECG signals to detect ECG and physiological signalcharacteristics predictive of malignant cardiac arrhythmias. Themonitoring device can communicate a warning signal to an external devicewhen arrhythmias are predicted. However, the Nappholz device is limitedto detecting ventricular tachycardias. Moreover, the ECG morphology ofmalignant cardiac tachycardias is well established and can be readilypredicted using on-board signal detection techniques. The Nappholzdevice is patient specific and is unable to automatically take intoconsideration a broader patient or peer group history for reference todetect and consider the progression or improvement of cardiovasculardisease. Additionally, the Nappholz device is unable to automaticallyself-reference multiple data points in time and cannot detect diseaseregression. Also, the Nappholz device must be implanted and cannotfunction as an external monitor. Finally, the Nappholz device isincapable of tracking the cardiovascular and cardiopulmonaryconsequences of any rhythm disorder.

Consequently, there is a need for an approach for remotely ordering andprioritizing multiple, related medical diseases and disorders using anautomated patient collection and analysis patient care system.Preferably, such an approach would identify a primary or index disorderfor diagnosis and treatment, while also aiding in the management ofsecondary disorders that arise as a consequence of the index event.

There is a further need for an automated, distributed system and methodcapable of providing medical health care services to remote patients viaa distributed communications means, such as a WAN, including theInternet. Preferably, such a system and method should be capable ofmonitoring objective “hard” physiological measures and subjective “soft”quality of life and symptom measures and correlating the two forms ofpatient health care data to order, prioritize and identify disorders anddisease.

SUMMARY OF THE INVENTION

The present invention provides a system and method for remotely orderingand prioritizing multiple, near-simultaneous health disorders using anautomated collection and analysis patient care system. The variousphysiological measures of individual patients are continuously monitoredusing implantable, external, or manual medical devices and the recordedphysiological measures are downloaded on a substantially regular basisto a centralized server system. Derived measures are extrapolated fromthe recorded measures. As an adjunct to the device-recorded measures,the patients may regularly submit subjective, quality of life andsymptom measures to the server system to assist identifying a change inhealth condition and to correlate with objective health care findings.Changes in patient status are determined by observing differencesbetween the various recorded, derived and quality of life and symptommeasures over time. Any changes in patient status are correlated tomultiple disorder candidates having similar abnormalities inphysiological measures for identification of a primary index disordercandidate.

An embodiment of the present invention is an automated collection andanalysis patient care system and method for ordering and prioritizingmultiple health disorders to identify an index disorder. A plurality ofmonitoring sets is retrieved from a database. Each of the monitoringsets include stored measures relating to patient information recordedand derived on a substantially continuous basis. A patient status changeis determined by comparing at least one stored measure from each of themonitoring sets to at least one other stored measure with both storedmeasures relating to the same type of patient information. Each patientstatus change is ordered in temporal sequence from least recent to mostrecent. A plurality of health disorder candidates categorized byquantifiable physiological measures of pathophysiologies indicative ofeach respective health disorder are evaluated and the health disordercandidate with the pathophysiology most closely matching those patientstatus changes which occurred least recently is identified as the indexdisorder, that is, the inciting disorder.

The present invention provides a capability to detect and track subtletrends and incremental changes in recorded patient medical informationfor automated multiple near-simultaneous health disorder diagnosis andanalysis. When coupled with an enrollment in a remote patient monitoringservice having the capability to remotely and continuously collect andanalyze external or implantable medical device measures, automatedmultiple health disorder diagnosis and analysis ordering andprioritizing become feasible.

Another benefit is improved predictive accuracy from the outset ofpatient care when a reference baseline is incorporated into theautomated diagnosis.

A further benefit is an expanded knowledge base created by expanding themethodologies applied to a single patient to include patient peer groupsand the overall patient population. Collaterally, the informationmaintained in the database could also be utilized for the development offurther predictive techniques and for medical research purposes.

Yet a further benefit is the ability to hone and improve the predictivetechniques employed through a continual reassessment of patient therapyoutcomes and morbidity rates.

Other benefits include an automated, expert system approach to thecross-referral, consideration, and potential finding or elimination ofother diseases and health disorders with similar or related etiologicalindicators.

Still other embodiments of the present invention will become readilyapparent to those skilled in the art from the following detaileddescription, wherein is described embodiments of the invention by way ofillustrating the best mode contemplated for carrying out the invention.As will be realized, the invention is capable of other and differentembodiments and its several details are capable of modifications invarious obvious respects, all without departing from the spirit and thescope of the present invention. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing an automated collection and analysispatient care system for ordering and prioritizing multiple healthdisorders in accordance with the present invention;

FIG. 2 is a database table showing, by way of example, a partial recordview of device and derived measures set records for remote patient carestored as part of a patient care record in the database of the system ofFIG. 1;

FIG. 3 is a database table showing, by way of example, a partial recordview of quality of life and symptom measures set records for remotepatient care stored as part of a patient care record in the database ofthe system of FIG. 1;

FIG. 4 is a database schema showing, by way of example, the organizationof a symptomatic event ordering set record for remote patient carestored as part of a symptomatic event ordering set for use in the systemof FIG. 1;

FIG. 5 is a block diagram showing the software modules of the serversystem of the system of FIG. 1;

FIG. 6 is a record view showing, by way of example, a set of partialpatient care records stored in the database of the system of FIG. 1;

FIG. 7 is a Venn diagram showing, by way of example, peer group overlapbetween the partial patient care records of FIG. 6;

FIGS. 8A-8B are flow diagrams showing a method for ordering andprioritizing multiple health disorders using an automated collection andanalysis patient care system in accordance with the present inventionFIG. 9 is a flow diagram showing the routine for retrieving referencebaseline sets for use in the method of FIGS. 8A-8B;

FIG. 10 is a flow diagram showing the routine for retrieving monitoringsets for use in the method of FIGS. 8A-8B;

FIG. 11 is a flow diagram showing the routine for selecting a measurefor use in the method of FIGS. 8A-8B;

FIGS. 12A-12B are flow diagrams showing the routine for evaluatingmultiple disorder candidates for use in the method of FIGS. 8A-8B; and

FIGS. 13A-13B are flow diagrams showing the routine for identifyingdisorder candidates for use in the method of FIGS. 8A-8B.

DETAILED DESCRIPTION

FIG. 1 is a block diagram showing an automated collection and analysispatient care system 10 for ordering and prioritizing multiple healthdisorders in accordance with the present invention. An exemplaryautomated collection and analysis patient care system suitable for usewith the present invention is disclosed in the related, commonlyassigned U.S. Pat. No. 6,312,378, issued Nov. 6, 2001, the disclosure ofwhich is incorporated herein by reference. Preferably, an individualpatient 11 is a recipient of an implantable medical device 12, such as,by way of example, an IPG, cardiovascular or heart failure monitor, ortherapeutic device, with a set of leads extending into his or her heartand electrodes implanted throughout the cardiopulmonary system.Alternatively, an external monitoring or therapeutic medical device 26,a subcutaneous monitor or device inserted into other organs, a cutaneousmonitor, or even a manual physiological measurement device, such as anelectrocardiogram or heart rate monitor, could be used. The implantablemedical device 12 and external medical device 26 include circuitry forrecording into a short-term, volatile memory telemetered signals storedfor later retrieval, which become part of a set of device and derivedmeasures, such as described below, by way of example, with reference toFIG. 2. Exemplary implantable medical devices suitable for use in thepresent invention include the Discovery line of pacemakers, manufacturedby Guidant Corporation, Indianapolis, Ind., and the Gem line of ICDs,manufactured by Medtronic Corporation, Minneapolis, Minn.

The telemetered signals stored in the implantable medical device 12 arepreferably retrieved upon the completion of an initial observationperiod and subsequently thereafter on a continuous, periodic (daily)basis, such as described in the related, commonly assigned U.S. Pat. No.6,221,011, issued Apr. 24, 2001, the disclosure of which is incorporatedherein by reference. A programmer 14, personal computer 18, or similardevice for communicating with an implantable medical device 12 can beused to retrieve the telemetered signals. A magnetized reed switch (notshown) within the implantable medical device 12 closes in response tothe placement of a wand 13 over the site of the implantable medicaldevice 12. The programmer 14 sends programming or interrogatinginstructions to and retrieves stored telemetered signals from theimplantable medical device 12 via RF signals exchanged through the wand13. Similar communication means are used for accessing the externalmedical device 26. Once downloaded, the telemetered signals are sent viaan internetwork 15, such as the Internet, to a server system 16 whichperiodically receives and stores the telemetered signals as devicemeasures in patient care records 23 in a database 17, as furtherdescribed below, by way of example, with reference to FIG. 2. Anexemplary programmer 14 suitable for use in the present invention is theModel 2901 Programmer Recorder Monitor, manufactured by GuidantCorporation, Indianapolis, Ind.

The patient 11 is remotely monitored by the server system 16 via theinternetwork 15 through the periodic receipt of the retrieved devicemeasures from the implantable medical device 12 or external medicaldevice 26. The patient care records 23 in the database 17 are organizedinto two identified sets of device measures: an optional referencebaseline 26 recorded during an initial observation period and monitoringsets 27 recorded subsequently thereafter. The monitoring measures sets27 are periodically analyzed and compared by the server system 16 toindicator thresholds 204 (shown in FIG. 5 below) corresponding toquantifiable physiological measures of pathophysiologies indicative ofmultiple, near-simultaneous disorders, as further described below withreference to FIG. 5. As necessary, feedback is provided to the patient11. By way of example, the feedback includes an electronic mail messageautomatically sent by the server system 16 over the internetwork 15 to apersonal computer 18 (PC) situated for local access by the patient 11.Alternatively, the feedback can be sent through a telephone interfacedevice 19 as an automated voice mail message to a telephone 21 or as anautomated facsimile message to a facsimile machine 22, both alsosituated for local access by the patient 11. Moreover, simultaneousnotifications can also be delivered to the patient's physician,hospital, or emergency medical services provider 29 using similarfeedback means to deliver the information.

The server system 10 can consist of either a single computer system or acooperatively networked or clustered set of computer systems. Eachcomputer system is a general purpose, programmed digital computingdevice consisting of a central processing unit (CPU), random accessmemory (RAM), non-volatile secondary storage, such as a hard drive or CDROM drive, network interfaces, and peripheral devices, including userinterfacing means, such as a keyboard and display. Program code,including software programs, and data are loaded into the RAM forexecution and processing by the CPU and results are generated fordisplay, output, transmittal, or storage, as is known in the art.

The database 17 stores patient care records 23 for each individualpatient to whom remote patient care is being provided. Each patient carerecord 23 contains normal patient identification and treatment profileinformation, as well as medical history, medications taken, height andweight, and other pertinent data (not shown). The patient care records23 consist primarily of two sets of data: device and derived measures(D&DM) sets 24 a, 24 b and quality of life (QOL) and symptom measuressets 25 a, 25 b, the organization and contents of which are furtherdescribed below with respect to FIGS. 2 and 3, respectively. The deviceand derived measures sets 24 a, 24 b and quality of life and symptommeasures sets 25 a, 25 b can be further logically categorized into twopotentially overlapping sets. The reference baseline 26 is a special setof device and derived reference measures sets 24 a and quality of lifeand symptom measures sets 25 a recorded and determined during an initialobservation period. Monitoring sets 27 are device and derived measuressets 24 b and quality of life and symptom measures sets 25 b recordedand determined thereafter on a regular, continuous basis. Other forms ofdatabase organization and contents are feasible.

The implantable medical device 12 and, in a more limited fashion, theexternal medical device 26, record patient medical information on aregular basis. The recorded patient information is downloaded and storedin the database 17 as part of a patient care record 23. Further patientinformation can be derived from the recorded patient information, as isknown in the art. FIG. 2 is a database table showing, by way of example,a partial record view 40 of device and derived measures set records41-85 for remote patient care stored as part of a patient care record inthe database 17 of the system of FIG. 1. Each record 41-85 storesphysiological measures, the time of day and a sequence number,non-exclusively. The physiological measures can include a snapshot oftelemetered signals data which were recorded by the implantable medicaldevice 12 or the external medical device 26, for instance, on perheartbeat, binned average or derived basis; measures derived from therecorded device measures; and manually collected information, such asobtained through a patient medical history interview or questionnaire.The time of day records the time and date at which the physiologicalmeasure was recorded. Finally, the sequence number indicates the orderin which the physiological measures are to be processed. Other types ofcollected, recorded, combined, or derived measures are possible, as isknown in the art.

The device and derived measures sets 24 a, 24 b (shown in FIG. 1), alongwith quality of life and symptom measures sets 25 a, 25 b, as furtherdescribed below with reference to FIG. 3, are continuously andperiodically received by the server system 16 as part of the on-goingpatient care monitoring and analysis function. These regularly collecteddata sets are collectively categorized as the monitoring sets 27 (shownin FIG. 1). In addition, select device and derived measures sets 24 aand quality of life and symptom measures sets 25 a can be designated asa reference baseline 26 at the outset of patient care to improve theaccuracy and meaningfulness of the serial monitoring sets 27. Selectpatient information is collected, recorded, and derived during aninitial period of observation or patient care, such as described in therelated, commonly assigned U.S. Pat. No. 6,221,011, issued Apr. 24,2001, the disclosure of which is incorporated herein by reference.

As an adjunct to remote patient care through the monitoring of measuredphysiological data via the implantable medical device 12 or externalmedical device 26, quality of life and symptom measures sets 25 a canalso be stored in the database 17 as part of the reference baseline 26,if used, and the monitoring sets 27. A quality of life measure is asemi-quantitative self-assessment of an individual patient's physicaland emotional well being and a record of symptoms, such as provided bythe Duke Activities Status Indicator. These scoring systems can beprovided for use by the patient 11 on the personal computer 18 (shown inFIG. 1) to record his or her quality of life scores for both initial andperiodic download to the server system 16. FIG. 3 is a database tableshowing, by way of example, a partial record view 95 of quality of lifeand symptom measures set records 96-111 for remote patient care storedas part of a patient care record in the database 17 of the system ofFIG. 1. Similar to the device and derived measures set records 41-85,each record 96-111 stores the quality of life (QOL) measure, the time ofday and a sequence number, non-exclusively.

Other types of quality of life and symptom measures are possible, suchas those indicated by responses to the Minnesota Living with HeartFailure Questionnaire described in E. Braunwald, ed., “Heart Disease—ATextbook of Cardiovascular Medicine,” pp. 452-454, W.B. Saunders Co.(1997), the disclosure of which is incorporated herein by reference.Similarly, functional classifications based on the relationship betweensymptoms and the amount of effort required to provoke them can serve asquality of life and symptom measures, such as the New York HeartAssociation (NYHA) classifications I, II, III and IV, also described inIbid.

The patient may also add non-device quantitative measures, such as thesix-minute walk distance, as complementary data to the device andderived measures sets 24 a, 24 b and the symptoms during the six-minutewalk to quality of life and symptom measures sets 25 a, 25 b.

On a periodic basis, the patient information stored in the database 17is evaluated and, if medically significant changes in patient wellnessare detected and medical disorders are identified. The sequence ofsymptomatic events is crucial. FIG. 4 is a database schema showing, byway of example, the organization of a symptomatic event ordering setrecord 120 for remote patient care stored as part of a symptomatic eventordering set 205 (shown in FIG. 5 below) for use in the system ofFIG. 1. By way of example, the record 120 stores and categorizes thegeneral symptomatic event markers for myocardial ischemia 121 into eventmarker sets: reduced exercise capacity 122, respiratory distress 123,and angina 124. In turn, each of the event marker sets 122-124 containmonitoring sets 125, 132, 138 and quality of life (QOL) sets 126, 133,139, respectively. Finally, each respective monitoring set and qualityof life set contains a set of individual symptomatic events whichtogether form a set of related and linked dependent measures. Here, themonitoring set 125 for reduced exercise capacity 122 contains decreasedcardiac output 127, decreased mixed venous oxygen score 128, anddecreased patient activity score 129 and the quality of life set 126contains exercise tolerance quality of life measure 130 and energy levelquality of life measure 131. Each symptomatic event contains a sequencenumber (Seq Num) indicating the order in which the symptomatic eventwill be evaluated, preferably proceeding from highly indicative to leastindicative. For example, reduced exercise capacity in congestive heartfailure is characterized by decreased cardiac output, as opposed to,say, reduced exercise capacity in primary pulmonary insufficiency wherecardiac output is likely to be normal. An absolute limit of cardiacoutput, indexed for weight, can therefore serve as an a priori marker ofcongestive heart failure in the absence of intravascular volumedepletion, i.e., low pulmonary artery diastolic pressure. Consequently,the markers of reduced exercise capacity in congestive heart failureorder cardiac output as the indicator having the highest priority with asequence number of “1.” Quality of life symptomatic events are similarlyordered.

FIG. 5 is a block diagram showing the software modules of the serversystem 16 of the system 10 of FIG. 1. Each module is a computer programwritten as source code in a conventional programming language, such asthe C or Java programming languages, and is presented for execution bythe CPU of the server system 16 as object or byte code, as is known inthe art. The various implementations of the source code and object andbyte codes can be held on a computer-readable storage medium or embodiedon a transmission medium in a carrier wave. The server system 16includes three primary software modules, database module 200, diagnosticmodule 201, and feedback module 203, which perform integrated functionsas follows.

First, the database module 200 organizes the individual patient carerecords 23 stored in the database 17 (shown in FIG. 1) and efficientlystores and accesses the reference baseline 26, monitoring sets 27, andpatient care data maintained in those records. Any type of databaseorganization could be utilized, including a flat file system,hierarchical database, relational database, or distributed database,such as provided by database vendors, such as Oracle Corporation,Redwood Shores, Calif.

Next, the diagnostic module 201 determines the ordering andprioritization of multiple near-simultaneous disorders to determine anindex disorder 212, that is, the inciting disorder, based on thecomparison and analysis of the data measures from the reference baseline26 and monitoring sets 27. The diagnostic module includes four modules:comparison module 206, analysis module 207, quality of life module 208,and sequencing module 209. The comparison module 206 compares recordedand derived measures retrieved from the reference baseline 26, if used,and monitoring sets 27 to indicator thresholds 204. The comparisonmodule 206 also determines changes between recorded and derived measuresretrieved from the reference baseline 26, if used, and monitoring sets27 to determine the occurrence of a symptomatic event using thesymptomatic event ordering set 205. The database 17 stores individualpatient care records 23 for patients suffering from various healthdisorders and diseases for which they are receiving remote patient care.For purposes of comparison and analysis by the comparison module 206,these records can be categorized into peer groups containing the recordsfor those patients suffering from similar disorders and diseases, aswell as being viewed in reference to the overall patient population. Thedefinition of the peer group can be progressively refined as the overallpatient population grows. To illustrate, FIG. 6 is a record viewshowing, by way of example, a set of partial patient care records storedin the database 17 for three patients, Patient 1, Patient 2, and Patient3. For each patient, three sets of peer measures, X, Y, and Z are shown.Each of the measures, X, Y, and Z could be either collected or derivedmeasures from the reference baseline 26, if used, and monitoring sets27.

The same measures are organized into time-based sets with Set 0representing sibling measures made at a reference time t=0. Similarly,Set n−2, Set n−1 and Set n each represent sibling measures made at laterreference times t=n−2, t=n−1 and t=n, respectively. Thus, for a givenpatient, such as Patient 1, serial peer measures, such as peer measureX₀ through X_(n), represent the same type of patient informationmonitored over time. The combined peer measures for all patients can becategorized into a health disorder- or disease-matched peer group. Thedefinition of disease-matched peer group is a progressive definition,refined over time as the number of monitored patients grows and thefeatures of the peer group become increasingly well-matched and uniform.Measures representing different types of patient information, such asmeasures X₀, Y₀, and Z₀, are sibling measures. These are measures whichare also measured over time, but which might have medically significantmeaning when compared to each other within a set for an individualpatient.

The comparison module 206 performs two basic forms of comparison. First,individual measures for a given patient can be compared to otherindividual measures for that same patient (self-referencing). Thesecomparisons might be peer-to-peer measures projected over time, forinstance, X_(n), X_(n-1), X_(n-2), . . . X₀, or sibling-to-siblingmeasures for a single snapshot, for instance, X_(n), Y_(n), and Z_(n),or projected over time, for instance, X_(n), Y_(n), Z_(n), X_(n-1),Y_(n-1), Z_(n-1), X_(n-2), Y_(n-2), Z_(n-2), . . . X₀, Y₀, Z₀. Second,individual measures for a given patient can be compared to otherindividual measures for a group of other patients sharing the samedisorder- or disease-specific characteristics (peer group referencing)or to the patient population in general (population referencing). Again,these comparisons might be peer-to-peer measures projected over time,for instance, X_(n), X_(n′), X_(n″), X_(n-1), X_(n-1′), X_(n-1″),X_(n-2), X_(n-2′), X_(n-2″) . . . X₀, X_(0′), X_(0″), or comparing theindividual patient's measures to an average from the group. Similarly,these comparisons might be sibling-to-sibling measures for singlesnapshots, for instance, X_(n), X_(n′), X_(n″), Y_(n), Y_(n′), Y_(n″),and Z_(n), Z_(n′), Z_(n″), or projected over time, for instance, Z_(n),X_(n′), X_(n″), Y_(n), Y_(n′), Y_(n″), Z_(n), Z_(n′), Z_(n″), X_(n-1),X_(n-1′), X_(n-1″), Y_(n-1), Y_(n-1′), Y_(n-1″), Z_(n-1), Z_(n-1′),Z_(n-1″), X_(n-2), X_(n-2′), X_(n-2″), Y_(n-2), Y_(n-2′), Y_(n-2″),Z_(n-2), Z_(n-2′), Z_(n-2″) . . . X₀, X_(0′), X_(0″), Y₀, Y_(0′),Y_(0″), and Z₀, Z_(0′), Z_(0″). Other forms of comparisons are feasible,including multiple disease diagnoses for diseases exhibiting similarphysiological measures or which might be a secondary disease candidate.

FIG. 7 is a Venn diagram showing, by way of example, peer group overlapbetween the partial patient care records 23 of FIG. 1. Each patient carerecord 23 includes characteristics data 350, 351, 352, includingpersonal traits, demographics, medical history, and related personaldata, for patients 1, 2 and 3, respectively. For example, thecharacteristics data 350 for patient 1 might include personal traitswhich include gender and age, such as male and an age between 40-45; ademographic of resident of New York City; and a medical historyconsisting of anterior myocardial infraction, congestive heart failureand diabetes. Similarly, the characteristics data 351 for patient 2might include identical personal traits, thereby resulting in partialoverlap 353 of characteristics data 350 and 351. Similar characteristicsoverlap 354, 355, 356 can exist between each respective patient. Theoverall patient population 357 would include the universe of allcharacteristics data. As the monitoring population grows, the number ofpatients with personal traits matching those of the monitored patientwill grow, increasing the value of peer group referencing. Large peergroups, well matched across all monitored measures, will result in awell known natural history of disease and will allow for more accurateprediction of the clinical course of the patient being monitored. If thepopulation of patients is relatively small, only some traits 356 will beuniformly present in any particular peer group. Eventually, peer groups,for instance, composed of 100 or more patients each, would evolve underconditions in which there would be complete overlap of substantially allsalient data, thereby forming a powerful core reference group for anynew patient being monitored with similar characters.

Referring back to FIG. 5, the analysis module 207 orders any patientstatus changes resulting from differences between physiological measuresand identifies an index disorder 212, as further described below withreference to FIGS. 8A-8B. Similarly, the quality of life module 208compares quality of life and symptom measures set 25 a, 25 b from thereference baseline 26 and monitoring sets 27, the results of which areincorporated into the comparisons performed by the analysis module 13,in part, to either refute or support the findings based on physiological“hard” data. The sequencing module 209 prioritizes patient changes inaccordance with pre-defined orderings, if used, or as modified byquality of life and symptom measures.

Finally, the feedback module 203 provides automated feedback to theindividual patient based, in part, on the patient status indicator 202generated by the diagnostic module 201.

In addition, the feedback module 203 determines whether any changes tointerventive measures are appropriate based on threshold stickiness(“hysteresis”) 210. The threshold stickiness 210 can limit thediagnostic measures to provide a buffer against transient, non-trendingand non-significant fluctuations in the various collected and derivedmeasures in favor of more certainty in diagnosis. As described above,the feedback could be by electronic mail or by automated voice mail orfacsimile. The feedback can also include normalized voice feedback, suchas described in the related, commonly assigned U.S. Pat. No. 6,203,495,issued Mar. 20, 2001, the disclosure of which is incorporated herein byreference.

In a further embodiment of the present invention, the feedback module203 includes a patient query engine 211, which enables the individualpatient 11 to interactively query the server system 16 regarding thediagnosis, therapeutic maneuvers, and treatment regimen. Similar patientquery engines 211 can be found in interactive expert systems fordiagnosing medical conditions. Using the personal computer 18 (shown inFIG. 1), the patient can have an interactive dialogue with the automatedserver system 16, as well as human experts as necessary, to self assesshis or her medical condition. Such expert systems are well known in theart, an example of which is the MYCIN expert system developed atStanford University and described in Buchanan, B. & Shortlife, E.,“RULE-BASED EXPERT SYSTEMS. The MYCIN Experiments of the StanfordHeuristic Programming Project,” Addison-Wesley (1984). The various formsof feedback described above help to increase the accuracy andspecificity of the reporting of the quality of life and symptomaticmeasures.

FIGS. 8A-8B are flow diagrams showing a method for ordering andprioritizing multiple health disorders 220 to identify an index disorder212 (shown in FIG. 5) using an automated collection and analysis patientcare system 10 in accordance with the present invention. A primarypurpose of this method is to determine what happened first to sortthrough multiple near-simultaneously-occurring disorders. For example,congestive heart failure can lead to myocardial insufficiency and viceversa. Moreover, congestive heart failure can complicate preexistingborderline pulmonary insufficiency. Similarly, when individuals haveborderline or sub-clinical congestive heart failure or myocardialischemia, primary pulmonary insufficiency, for example, an exacerbationof chronic bronchitis, can lead to fulminant congestive heart failure,myocardial ischemia, or both. Atrial fibrillation can complicate all ofthe above-noted disorders, either as a result of or as a precipitant ofone of the foregoing disorders.

The sequence of the events resulting from changes in physiologicalmeasures, as may be corroborated by quality of life and symptommeasures, is crucial. In patients with more than one disease, certainphysiological measures are the key to identifying the index disorder;however, these same physiological measures might not be uniquelyabnormal to any particular disorder. Consequently, a diagnosis dependingupon these particular non-diagnostic physiological measures will be moredependent upon the ordering of changes or measure creep than thephysiological measure value itself. For example, cardiac output 49(shown in FIG. 2) or its derivatives can decrease in congestive heartfailure, myocardial ischemia, respiratory insufficiency, or atrialfibrillation. However, decreased cardiac output in myocardial ischemiawould be preceded by an abnormality of ST elevation (ST segment measures77), T-wave inversion (T wave measures 79), troponin increase (serumtroponin 74), wall motion abnormality onset (left ventricular wallmotion changes 58), increased coronary sinus lactate production 53, andpossibly QRS widening (as a marker of myocardial ischemia) (QRS measures70).

Similarly, decreased cardiac output in respiratory insufficiency wouldbe preceded by other physiological measures, which, although not asdiagnostic as myocardial ischemia, can include, for example, elevationin respiratory rate 72, elevation in minute ventilation 60, elevation intidal volume (derived from minute ventilation 60 and respiratory rate72), increase in transthoracic impedance 81 consistent with increasedaeration of the lungs, decrease in QT interval 71 (or other surrogatefor increase in temperature), spikes in the activity sensor 63 orpulmonary artery pressures 66, 68 as markers of cough 103, decrease inarterial partial pressure of oxygen 43, and decreases in arterialpartial pressure of carbon dioxide 42 in probable association with lowor normal pulmonary artery diastolic pressure 67. Once pulmonaryinsufficiency onsets, the subsequent fall in arterial oxygen pressuremay be enough to trigger myocardial ischemia, in the case of a patientwith borderline coronary artery disease, or to trigger congestive heartfailure, in the case of a patient with borderline left ventriculardysfunction. However, these disorders would be identified as secondarydisorders with the aid of the present invention.

Note that the foregoing interrelationships between the respectivephysiological measures for diagnosing and treating congestive heartfailure, myocardial ischemia, respiratory insufficiency and atrialfibrillation are merely illustrative and not exhaustive. Moreover, otherheretofore unidentified disorders can also share suchinterrelationships, as is known in the art, to cover, non-specifieddisorder diagnostics, such as for diabetes, hypertension, sleep-apnea,stroke, anemia, and so forth.

Thus, the method begins by retrieving the reference baseline 26 (block221) and monitoring sets 27 (block 222) from the database 17, as furtherdescribed below with reference to FIGS. 9 and 10, respectively. Eachmeasure in the device and derived measures sets 24 a, 24 b (shown inFIG. 1) and quality of life and symptom measures sets 25 a, 25 b, ifused, is iteratively processed (blocks 223-227). These measures areobtained from the monitoring sets 27 and, again if used, the referencebaseline 26. During each iteration loop, a measure is selected (block224), as further described below with reference to FIG. 11. If themeasure has changed (block 225), the timing and magnitude of the changeis determined and logged (block 226). Iterative processing (blocks223-227) continues until all measures have been selected at which timeany changes are ordered in temporal sequence (block 228) from leastrecent to most recent. Next, multiple disorder candidates are evaluated(block 229) and the most closely matching disorder candidates, includinga primary or index disorder and any secondary disorders, are identified(block 230), as further described below respectively in FIGS. 12A-12Band 13A-13B. A patient status indicator 202 for any identifieddisorders, including the primary or index disorder 212 (shown in FIG.5), is provided (block 231) to the patient regarding physicalwell-being, disease prognosis, including any determinations of diseaseonset, progression, regression, or status quo, and other pertinentmedical and general information of potential interest to the patient.

Finally, in a further embodiment, if the patient submits a query to theserver system 16 (block 232), the patient query is interactivelyprocessed by the patient query engine (block 233). Similarly, if theserver elects to query the patient (block 234), the server query isinteractively processed by the server query engine (block 235). Themethod then terminates if no further patient or server queries aresubmitted.

In the described embodiment, both the time at which a change occurredand the relative magnitude of the change are utilized for indexing thediagnosis. In addition, related measures are linked into dependent setsof measures, preferably by disorder and principal symptom findings(e.g., as shown in FIG. 4), such that any change in one measure willautomatically result in the examination of the timing and magnitude inany changes in the related measures. For example, ST segment changes(measure 76 shown in FIG. 2) can fluctuate slightly with or withoutsevere consequences in patient condition. A 0.5 SD change in ST segment,for instance, is generally considered modest when not tied to otherphysiological measure changes. However, a 0.5 SD ST segment changefollowed by a massive left ventricular wall motion change 58 canindicate, for example, left anterior descending coronary arteryocclusion. The magnitude of change therefore can help determine theprimacy of the pertinent disorder and the timing and sequence of relatedchanges can help categorize the clinical severity of the inciting event.

Also, an adjustable time window can be used to detect measure creep bywidening the time period over which a change in physiological measurecan be observed. For example, mean cardiac output 49 may appearunchanging over a short term period of observation, for instance, oneweek, but might actually be decreasing subtly from month-to-monthmarking an insidious, yet serious disease process. The adjustable timewindow allows such subtle, trending changes to be detected.

Similarly, a clinically reasonable time limit can be placed on theadjustable time window as an upper bound. The length of the upper boundis disease specific. For example, atrial fibrillation preceded bycongestive heart failure by 24 hours is correlative; however, atrialfibrillation preceded by congestive heart failure one year earlier willlikely not be considered an inciting factor without more closelytemporally linked changes. Similarly, congestive heart failure secondaryto atrial fibrillation can occur more gradually than congestive heartfailure secondary to myocardial ischemia. The upper bound thereforeserves to limit the scope of the time period over which changes tophysiological measures are observed and adjusted for disease-specificdiagnostic purposes.

FIG. 9 is a flow diagram showing the routine for retrieving referencebaseline sets 221 for use in the method of FIGS. 8A-8B. The purpose ofthis routine is to retrieve the appropriate reference baseline sets 26,if used, from the database 17 based on the types of comparisons beingperformed. First, if the comparisons are self referencing with respectto the measures stored in the individual patient care record 23 (block240), the reference device and derived measures set 24 a and referencequality of life and symptom measures set 25 a, if used, are retrievedfor the individual patient from the database 17 (block 241). Next, ifthe comparisons are peer group referencing with respect to measuresstored in the patient care records 23 for a health disorder- ordisease-specific peer group (block 242), the reference device andderived measures set 24 a and reference quality of life and symptommeasures set 25 a, if used, are retrieved from each patient care record23 for the peer group from the database 17 (block 243). Minimum,maximum, averaged, standard deviation (SD), and trending data for eachmeasure from the reference baseline 26 for the peer group, are thencalculated (block 244). Finally, if the comparisons are populationreferencing with respect to measures stored in the patient care records23 for the overall patient population (block 245), the reference deviceand derived measures set 24 a and reference quality of life and symptommeasures set 25 a, if used, are retrieved from each patient care record23 from the database 17 (block 246). Minimum, maximum, averaged,standard deviation, and trending data for each measure from thereference baseline 26 for the peer group is then calculated (block 247).The routine then returns.

FIG. 10 is a flow diagram showing the routine for retrieving monitoringsets 222 for use in the method of FIGS. 8A-8B. The purpose of thisroutine is to retrieve the appropriate monitoring sets 27 from thedatabase 17 based on the types of comparisons being performed. First, ifthe comparisons are self referencing with respect to the measures storedin the individual patient care record 23 (block 250), the device andderived measures set 24 b and quality of life and symptom measures set25 b, if used, are retrieved for the individual patient from thedatabase 17 (block 251). Next, if the comparisons are peer groupreferencing with respect to measures stored in the patient care records23 for a health disorder- or disease-specific peer group (block 252),the device and derived measures set 24 b and quality of life and symptommeasures set 25 b, if used, are retrieved from each patient care record23 for the peer group from the database 17 (block 253). Minimum,maximum, averaged, standard deviation, and trending data for eachmeasure from the monitoring sets 27 for the peer group is thencalculated (block 254). Finally, if the comparisons are populationreferencing with respect to measures stored in the patient care records23 for the overall patient population (block 255), the device andderived measures set 24 b and quality of life and symptom measures set25 b, if used, are retrieved from each patient care record 23 from thedatabase 17 (block 256). Minimum, maximum, averaged, standard deviation,and trending data for each measure from the monitoring sets 27 for thepeer group is then calculated (block 257). The routine then returns.

FIG. 11 is a flow diagram showing the routine for selecting a measure224 for use in the method of FIGS. 8A-8B. The purpose of this routine isto select a measure from the device and derived measures sets 24 a, 24 bor quality of life and symptom measures sets 25 a, 25 b in anappropriate order. Thus, if the measures are ordered in a pre-definedsequence (block 260), the next sequential measure is selected forcomparison in the method of FIGS. 8A-8B (block 261). Otherwise, the nextmeasure appearing in the respective measures set is selected (block262). The routine then returns.

FIGS. 12A-12B are flow diagrams showing the routine for evaluatingmultiple disorder candidates 229 for use in the method of FIGS. 8A-8B.The purpose of this routine is to generate a log of findings based oncomparisons of patient status changes to the various pathophysiologicalmarkers characteristic of each of the multiple, near-simultaneousdisorders. Quality of life and symptom measures can be used in two ways.First, changes in a quality of life and symptom measures can serve as astarting point in diagnosing a disorder. For instance, shortness ofbreath 93 (shown in FIG. 3) can serve as a marker of respiratorydistress congestive heart failure. Second, quality of life and symptommeasures can corroborate disorder findings. In the described embodiment,the use of quality of life and symptom measures as a diagnostic startingpoint is incorporated into the analysis by prioritizing the importanceof related physiological measure changes based on the least recentquality of life measure change. For example, if shortness of breath 93followed the corresponding physiological changes for respiratorydistress congestive heart failure, that is, decreased cardiac output 127(shown in FIG. 4), decreased mixed venous oxygen score 128, anddecreased patient activity score 129, would be assigned a higherpriority than the other physiological measures. Similarly, in thedescribed embodiment, certain physiological measures can also beassigned a higher priority independent of any changes to the quality oflife and symptom measures.

Thus, if quality of life and symptom measures are included in thediagnostic process (block 270) and the related physiological measuresare prioritized based on quality of life changes (block 271), thechanges in physiological measures are sorted according to the quality oflife-assigned priorities (block 272). Alternatively, if quality of lifeand symptom measures are not being used (block 270) or the changes inphysiological measures are not assigned quality of life priorities(block 271), the physiological changes could still be independentlyprioritized (block 273). If so, the physiological measures are sortedaccording to the non-quality of life assigned-priorities (block 274).

Next, each of the multiple disorder candidates and each measure in theirrespective sets of physiological measures, including any linkedmeasures, and, if used, quality of life and symptom measures, areiteratively processed in a pair of nested processing loops (blocks275-284 and 277-282, respectively). Other forms of flow control arefeasible, including recursive processing. Each disorder candidate isiteratively processed in the outer processing loop (blocks 275-284).During each outer processing loop, a disorder candidate is selected(block 276) and each of the physiological measures, and quality of lifeand symptom measures, if used, are iteratively processed in the innerprocessing loop (blocks 277-282). Each measure is assigned a sequencenumber, such as shown, by way of example, in each symptomatic eventordering set records 121-152 (shown in FIG. 4) for a principal symptomfinding of the disorder candidate. The measures are evaluated insequential order for timing and magnitude changes (block 278). If themeasure is linked to other related measures (block 279), the relatedmeasures are also checked for timing and magnitude changes (block 280).Any matched pathophysiological findings are logged (block 281). Theoperations of evaluating and matching pathophysiological measures (box283) for diagnosing congestive heart failure, myocardial infarction,respiratory distress, and atrial fibrillation are described in related,commonly assigned U.S. Pat. No. 6,336,903, entitled “AutomatedCollection And Analysis Patient Care System And Method For DiagnosingAnd Monitoring Congestive Heart Failure And Outcomes Thereof,” issuedJan. 8, 2002; U.S. Pat. No. 6,368,284, entitled “Automated CollectionAnd Analysis Patient Care System And Method For Diagnosing AndMonitoring Myocardial Ischemia And Outcomes Thereof,” issued Apr. 9,2002; U.S. Pat. No. 6,398,728, entitled “Automated Collection AndAnalysis Patient Care System And Method For Diagnosing And MonitoringRespiratory Insufficiency And Outcomes Thereof,” issued Jun. 4, 2002;and U.S. Pat. No. 6,411,840, entitled “Automated Collection And AnalysisPatient Care System And Method For Diagnosing And Monitoring TheOutcomes Of Atrial Fibrillation,” issued Jun. 25, 2002, the disclosuresof which are incorporated herein by reference. Note the evaluation andmatching of pathophysiological measures 283 can also encompass diseaseworsening and improvement.

Iterative processing of measures (blocks 277-282) continues until allpathophysiological measures of the disorder have been evaluated,whereupon the next disorder candidate is selected. Iterative processingof disorders (blocks 275-284) continues until all disorders have beenselected, after which the routine returns.

FIGS. 13A-13B are flow diagrams showing the routine for identifyingdisorder candidates 230 for use in the method of FIGS. 8A-8B. Thepurpose of this routine is to identify a primary or index disorder 212and any secondary disorder(s). At this stage, all changes inphysiological measures and quality of life and symptom measures havebeen identified and any matches between the changes and thepathophysiological indicators of each near-simultaneous disorder havebeen logged. The findings must now be ordered and ranked. First, thematched findings are sorted into temporal sequence (block 290),preferably from least recent to most recent. Next, each of the findingsand each of the disorder candidates are iteratively processed in a pairof nested processing loops (blocks 291-298 and 292-297, respectively).Other forms of flow control are feasible, including recursiveprocessing. Each finding is iteratively processed in the outerprocessing loop (blocks 291-298) beginning with the least recentfinding. For each finding, each disorder candidate is iterativelyprocessed during each inner processing loop (blocks 292-297) todetermine the relative strength of any match. If the disorder candidatehas a pathophysiological indicator which matches the current finding(block 293), the disorder candidate is ranked above any other disordercandidate not matching the current finding (block 294). This form ofranking ensures the disorder candidate with a pathophysiologicalindicator matching a least recent change in measure is considered aheadof other disorder candidates which may be secondary disorders. Inaddition, if the measure is prioritized (block 295), that is, themeasure is a member of a group of related linked measures which havealso changed or is an a priori measure, the ranking of the disordercandidate is increased (block 296). Iterative processing of disorders(blocks 292-297) continues until all disorder candidates have beenconsidered. Similarly, iterative processing of findings (blocks 291-298)continues until all findings have been evaluated, whereupon the highestranking disorder candidate is identified as the primary or indexdisorder 212 (shown in FIG. 5) (block 299). If other disorders rankclose to the primary or index disorder and similarly reflect a strongmatch to the set of findings, any secondary disorder(s) are likewiseidentified and temporally ranked (block 300). The routine then returns.

While the invention has been particularly shown and described asreferenced to the embodiments thereof, those skilled in the art willunderstand that the foregoing and other changes in form and detail maybe made therein without departing from the spirit and scope of theinvention.

1. An analysis system for providing an index disorder for use inautomated patient care, comprising: a server comprising: a databaseconfigured to store a set of device measures regularly recorded by amedical device for a patient under automated patient care; a set ofreference baseline measures recorded during an initial observationperiod; and indicator thresholds corresponding to quantifiablephysiological measures of pathophysiologies; and a diagnostic moduleconfigured to retrieve the device measures and the reference baselinemeasures from the database, comprising: a comparison module configuredto iteratively process the device measures and the reference baselinemeasures with the indicator thresholds and configured to identifymultiple near-simultaneous disorders from said iterative process,wherein each iterative loop of the iterative process comprises analyzingthe device measures and the reference baseline measures for changesoccurring over time; and an analysis module configured to analyze andorder the changes and the multiple-near simultaneous disorders occurringover time and to identify the index disorder based on said analysismodules analyzing and ordering.
 2. An analysis system according to claim1, the database further configured to store processed raw andphysiological measures; the analysis module further comprising: anordering component configured to compare a plurality of the processedraw and physiological measures to quantify one or more changes inpathophysiology, and to order the pathophysiology changes in temporalsequence.
 3. An analysis system according to claim 1, the comparisonmodule further comprising: a categorizing component configured tocategorize health disorder candidates by pathophysiology, and toidentify the health disorder candidate having a pathophysiologysubstantially comparable to a change in pathophysiology that occurredeither substantially least or most recently.
 4. An analysis systemaccording to claim 3, the database further comprising a plurality ofpreviously-related pathophysiologies for at least one health disordercandidate; the diagnostic module further comprising an evaluationcomponent configured to sort the plurality of previously-relatedpathophysiologies for at least one health disorder candidate into asymptomatic event ordering set, and to evaluate each pathophysiology inthe symptomatic event ordering set in response to a change in thepathophysiology being evaluated.
 5. An analysis system according toclaim 1, the database further configured to store processed raw andphysiological measures; the comparison module further configured tocompare at least one of the processed raw and physiological measures toat least one other of the processed raw and physiological measures thatwere previously recorded.
 6. An analysis system according to claim 1,the comparison module further configured to prioritize pathophysiologychanges using a predefined ordering, wherein the physiological changeshave different priorities, and wherein the comparison component isfurther configured to compare the pathophysiology changes having higherpriorities that occurred least recently before the pathophysiologychanges having lower priorities.
 7. An analysis system according toclaim 1, the server further comprising: a feedback module comprising ahysteresis component configured to track temporal changes inpathophysiology.
 8. An analysis system according to claim 1, wherein theindicator thresholds correspond to a pathophysiology indicative of atleast one of congestive heart failure, myocardial ischemia, respiratoryinsufficiency, and atrial fibrillation health disorders; and thediagnostic module configured to test pathophysiology changes against theindicator thresholds to determine whether a change in pathophysiologyhas occurred.